26 Jun 2024
 | 26 Jun 2024
Status: this preprint is open for discussion.

Exploring the influence of spatio-temporal scale differences in Coupled Data Assimilation

Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon

Abstract. Identifying the optimal strategy for initializing coupled climate prediction systems is challenging due to the spatio-temporal scale separation and disparities in the observational network. We aim to clarify when strongly coupled data assimilation (SCDA) is preferable to weakly coupled data assimilation (WCDA). We use a two-components coupled Lorenz-63 system and the Ensemble Kalman Filter (EnKF) to compare WCDA and SCDA for diverse spatio-temporal scale separations and observational networks – only in the atmosphere, the ocean, or both components.  When both components are observed, SCDA and WCDA yield similar performances. However, sometimes SCDA performs marginally worse due to its higher sensitivity (as opposed to WCDA) to key approximations in the EnKF – linear analysis update and sampling error. When observations are only in one of the components, SCDA systematically outperforms WCDA. The spatio-temporal scale separation determines SCDA's performance in this scenario and the largest improvements are found when the observed component has a smaller spatial scale. This suggests that SCDA of fast atmospheric observations can potentially improve the large-slow ocean component. Conversely, observations of the fine ocean can improve the large atmosphere at a comparable temporal scale. However, when both components are highly chaotic, and the observed component's spatial scale is the largest, SCDA does not improve over WCDA. In such a case, the cross-updates may become too sensitive to data assimilation approximations. 

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Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon

Status: open (until 21 Aug 2024)

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Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon
Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon


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Short summary
We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage.
We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.